WO2025026560A1

WO2025026560A1 - A method of providing microenvironment-specific data to unidentified users

Info

Publication number: WO2025026560A1
Application number: PCT/EP2023/071537
Authority: WO
Inventors: Axel BELLIENO
Original assignee: Bellieno Media Consulting GmbH
Current assignee: Bellieno Media Consulting GmbH
Priority date: 2023-08-03
Filing date: 2023-08-03
Publication date: 2025-02-06
Anticipated expiration: 2026-02-03

Abstract

This disclosure relates to a method of providing microenvironment-specific data output to an unidentified user for a website microenvironment or an app microenvironment, in response to a request of an unidentified user. The method comprises building microenvironment user data for at least one microenvironment, involving the steps of: providing a hierarchical structure of microenvironments for a website or an app, the hierarchical structure including at least one microenvironment and a superordinate microenvironment that is defined to be hierarchically superordinate to the microenvironment, collecting first party data as microenvironment user data for the at least one microenvironment, based on first party data of identified users accessing the at least one microenvironment, allocating superordinate microenvironment-specific data to the superordinate microenvironment, providing user groups of identified users and allocating different user-specific data for the microenvironment to different user groups, identifying at least one predominant user group for the microenvironment based on the microenvironment user data; and, upon a request to access the microenvironment by an unidentified user, generating and outputting microenvironment-specific data output including user-specific data, allocated to the at least one predominant user group for the microenvironment.

Description

A Method of Providing Microenvironment-Specific Data to Unidentified Users

Technical Field

The present disclosure relates to a method of providing microenvironment-speci fic data output to an unidenti fied user for a website microenvironment or app microenvironment , such as a URL or a group of URLs of a website or an app screen, in response to a user request of an unidenti fied user .

The disclosure also relates to a data carrier with a program stored thereon, and to a web server .

Technical Background

The ef ficient processing of large amounts information has become of maj or importance for practically any society . While the amount of accessible information is almost without limits in terms of practical matters , a problem that is growing in its signi ficance , is the problem of distraction, side-lining the ef ficient and goal-oriented processing of information, and diluting what people are faced with on a daily basis .

The diluting of what would be most relevant to a user is to be found in many environments of fering user interfaces , through which a user interacts with a digital environment . This in turn results in massive increases of data trans fer .

First and foremost , an environment suf fering from the mentioned dilution of data, is the internet . When users surf websites , they often spend large fractions of their time browsing through what is personally of limited or no meaning, while progressing only slowly towards the actual aims . This results in an enormous amount of website traf fic which corresponds to what one might refer to as "digital waste" .

A method that allows reducing data traf fic, relies on tracking users . This allows building a profile and then selectively guiding users to what is of relevance to them more ef ficiently, by correlating data output to users with what is known about them due to tracking . However, the tracking of users is associated with ethical concerns relating to privacy and, depending on the circumstances , nowadays to an increasing degree legally prohibited under many j urisdictions .

Another method to reduce data traf fic lies in letting users register . The registering can, for example , be done on a particular website on the internet , or within the environment of an app on a mobile phone , a tablet , or on a smart TV . The information about a registered user can then be used to influence the data that is output to the user in di f ferent environments . The information that is collected and associated with a speci fic user ID of a registered user, is also referred to as first party cookie data . However, the method of relying on first party cookie data to reduce data traf fic, is limited to registered users . Moreover, based on ethical considerations , there is an increasing tendency towards prohibition of urging or even forcing users to register when using websites or apps , and the like .

For digital environments with user interfaces , there would be a desire for methods that allow to reduce the overall data traf fic by promoting the principles of simplicity and ef ficacy . I f the single user can be helped by reducing the amount o f dilution encountered, then the society as a whole can be helped by reducing the amount of digital waste .

There is , hence , a need for an improved method of generating data output for users in environments such as the internet or apps on electronic devices that address at least one of the above-mentioned shortcomings.

Summary

Aspects of the above-mentioned object are achieved by a method of providing microenvironment-specific data output to an unidentified user for a website microenvironment or an app microenvironment. in response to a user request of an unidentified user, in accordance with the present disclosure.

A website microenvironment may, for example, be a particular unified resource locator (URL) . A website microenvironment may also be a group of URLs, a host, a domain name, a domain, sublevel domain, a path, and/or another location of a website, etc. A website may comprise several microenvironments, some of which may be URLs, others being groups of URLs, etc.

A screen microenvironment may be a screen of an app, such as a log-in screen, a home screen, an onboarding screen, a profile screen, a settings screen, a feed screen, a calendar screen, a map screen, a conversational screen, a splash screen, a product card screen, a content screen, a checkout screen, a statistics screen, and a terms and conditions screen.

The term microenvironment-specific data output may be understood as a reference to a data output that is specific to a microenvironment. That is, different data may be output for a different microenvironment. However, the data does not need to be specific for every microenvironment that forms part of a website or app, but it is specific for certain embodiments, at least once a sufficient amount of user data is available.

The method may comprise building microenvironment user data for at least one microenvironment. The building of microenvironment user data is performed for the at least one microenvironment, and may be (independently) performed for other microenvironments . The building of microenvironment user data may also be referred to as dynamically building microenvironment data, as the process is dynamic in that the building process may be continued whenever identi fied users access the microenvironment .

The building of microenvironment user data may comprise the step of defining ( this may be a step of providing) a hierarchical structure of microenvironments for a website or an app, the hierarchical structure including the at least one microenvironment . The hierarchical structure serves as a definition of a microenvironment being superordinate to another microenvironment . This may, in particular, correspond to the structure of a website or an app . For example , a homepage may correspond to a URL that is superordinate to a page ( a URL or a group of URLs ) that a user can access by clicking somewhere on the homepage . In the case of an app, a home screen may, e . g . , be superordinate to another screen .

The hierarchical structure may in particular define that there is a microenvironment that is superordinate to the at least one microenvironment .

The method may comprise the step of collecting first party data as microenvironment user data for the at least one microenvironment , based on first party data of identi fied users accessing the at least one microenvironment . The identi fied users may in particular refer to users that have given their consent to the collection of user data . For example , the identi fied users may be identi fied by uni fied IDs or net IDs .

The method may comprise the step of allocating superordinate microenvironment-speci fic data to the superordinate microenvironment . The allocating may or may not include collecting first party data for the superordinate data, or a collecting of first party data may comprise the step of allocating data . The allocating data may in some cases be performed relying on data that is provided (without any collecting taking place as part of the method) . For example, data may be used that was collected in a different context (e.g., on the basis of a different app or website) .

The method may comprise steps of providing (e.g., at the outset of applying the method to a website or app: defining) user groups of identified users and allocating different userspecific data for the microenvironment to different user groups. The user groups may comprise different numbers of users, and a group may, e.g., also comprise only one user (or may, e.g., at times, comprise no users) .

A user group may be defined by one or several common features of identified users (e.g., male or female, an age group, etc.) .

The method may comprise identifying at least one predominant user group, optionally several predominant user groups, or a distribution of user groups, for the microenvironment based on the microenvironment user data. The identifying may comprise applying a statistical method to the microenvironment user data .

The method may comprise, upon a request to access the microenvironment by an unidentified user, generating and outputting microenvironment-specific data output including user-specific data, allocated to the at least one predominant user group, optionally to at least one of the several predominant user groups or at least one of the distribution of user groups, for the microenvironment.

The method may comprise, upon a request to access the microenvironment by an unidentified user, generating and outputting microenvironment-specific data output including at least one of: user-specific data, allocated to the at least one predominant user group, optionally to at least one of the several predominant user groups or at least one of the distribution of user groups, for the microenvironment, and superordinate microenvironment-specific data allocated to the superordinate microenvironment. The method comprises, at least at some point in time, the generating of microenvironmentspecific data that includes user specific-data for the microenvironment. The microenvironment-specific data may, sometimes or always, consist of user specific-data for the microenvironment. Subsequently, the generating microenvironment-specific data output may, at times, include (or consist of) superordinate microenvironment-specific data allocated to the superordinate microenvironment, but not include the user specific-data for the microenvironment.

How specific the data is with respect to the microenvironment may be referred to as a resolution of the method in the sense that the more microenvironments that are provided with microenvironment-specific data output, the higher the resolution .

The generating and outputting of microenvironment-specific data may reduce the amount of information being processed when users, and especially also unidentified users, access a microenvironment. The average reduction of network traffic may of course be extended to several microenvironments and, optionally, to a website or websites, and likewise, to different microenvironments of an app, or to an entire app .

Concurrently, the method in question may promote privacy and prevent the temptation of any form of tracking without consent by users, as there is an alternative in the form of the method in accordance with the present disclosure available.

In summary, the efficiency of data processing may be increased, and the overall network traffic may be reduced.

Optionally, the method may comprise a step of verifying whether there is a sufficient amount of microenvironment user data available for the at least one microenvironment. The method may comprise, when there is a sufficient amount of microenvironment user data available, applying a statistical method to the microenvironment user data for the microenvironment to identify the at least one predominant user group, optionally the several predominant user groups or the distribution of user groups.

The identifying need not depend on the sufficient amount of microenvironment user data being available, but it is at least also performed in some cases when it is available. In other words, the condition that it is performed when there is a sufficient amount of data available, does not exclude for all embodiments that the identifying may also already be performed before a sufficient amount of data is available. However, for other embodiments, the identifying is performed upon verifying that a sufficient amount of data is available. For those embodiments, the "when" may also be re-expressed as an "if" or "upon the condition of".

The identifying of the at least one predominant user group, optionally the several predominant user groups or the distribution of user groups may comprise verifying whether a number of data entries of first party data for the microenvironment is equal to or larger than a threshold. The threshold may, e.g., be in the range of 40-120 entries, 50- 100, or 70-90 entries. Each entry may count as one user entry, irrespective of whether the same user has multiple entries (due to having accessed a microenvironment a plurality of times) . In other cases, an entry of the same user may only be counted once, i.e., different entries then belong to different users .

The method may comprise, upon the request to access the microenvironment by the unidentified user, when there is a sufficient amount of microenvironment user data available, generating the microenvironment-specific data output such that it includes user-speci fic data al located to the at least one predominant user group, optionally at least one of the several predominant user groups at least one of the distribution of user groups . This way, the user-speci fic data is included in case it carries some weight in the sense that the user-speci fic data is tailored to users that indeed may well represent the type of users accessing the microenvironment . At least , the data trans fer may in this manner be made more ef ficient than i f the there is less speci ficity to the output , so that the amount of digital waste is on average higher than when following this method .

The method may comprise , when there is not a suf ficient amount of microenvironment user data available , generating the microenvironment-speci fic data output such that it includes superordinate microenvironment-speci fic data . This way, the lack of knowledge about users accessing the microenvironment may be partially or fully compensated, as one may presume that there is correlation between users accessing the microenvironment and the superordinate microenvironment . Put di f ferently, user-speci fic data relating to the superordinate microenvironment may be more representative than the userspeci fic data relating to the microenvironment , when there is not a suf ficient amount of the latter available ( yet ) . Due to the dynamic building of data, there is an increasing amount of information available over time . In so far, the method in accordance with the present disclosure may be considered to generate data that is laid over the structure of a website or an app with a resolution that increases over time due to usage by registered users .

The method may comprise allocating di f ferent superordinate microenvironment-speci fic data to di f ferent user groups , to define di f ferent user-speci fic data for the superordinate microenvironment . This may increase the accuracy of the output when it includes superordinate microenvironment-speci fic data . This may reduce digital waste in the sense that the chance is statistically higher that non-registered users arrive at the information relevant for them more quickly than when relying on a more randomi zed output approach .

The method may comprise collecting first party data as superordinate microenvironment user data for the superordinate microenvironment based on first party data, such as uni fied IDs or net IDs , of identi fied users accessing the superordinate microenvironment . In so far, the method may work analogously for the superordinate microenvironment as for the microenvironment as described above . In addition, again, the identi fied users may in particular refer to users that have given their consent to the collection of user data . For example , the identi fied users may be identi fied by uni fied IDs or net IDs .

The method may comprise applying the statistical method ( i . e . , the same statistical method as the one used in the context of the microenvironment as described above ) or another statistical method to the superordinate microenvironment user data to identi fy at least one predominant user group, optionally several predominant user groups , or a distribution of user groups , for the superordinate microenvironment . In so far, the method may work analogously for the superordinate microenvironment as for the microenvironment as described above .

The method may comprise , upon the request to access the microenvironment by the unidenti fied user, when there is not a suf ficient amount of microenvironment user data available , generating the microenvironment-speci fic data output such that it includes user-speci fic data, allocated to the at least one predominant user group, optional ly to at least one of the several predominant user groups or at least one of the distribution of user groups , for the superordinate microenvironment . In so far, the method may work analogously for the superordinate microenvironment as for the microenvironment as described above . The method may rely on data relating to a microenvironment that is superordinate to the superordinate microenvironment ( yet higher up in the structural hierarchy defined for a plurality of microenvironments of a website or an app ) .

Optionally, a weight factor may be assigned to the microenvironment user data of the microenvironment and a superordinate weight factor may be assigned to the superordinate microenvironment user data of the superordinate microenvironment . The method may comprise identi fying the at least one predominant user group, optionally the several predominant user groups or the distribution of user groups , for the microenvironment , on the basis of the microenvironment user data weighted by the weight factor and the superordinate microenvironment user data weighted by the superordinate weight factor . This may further decrease the average network traf fic, as the consideration not only of a microenvironment , but also of the superordinate microenvironment , according to a key in terms of relative weights , may allow to more precisely optimi ze the output on the basis of data assembled on the basis of registered users .

The weight factor may be set in function of a number of data entries of first party data for the microenvironment . Likewise , the superordinate weight factor may be set in function of a number of data entries of first party data for the superordinate microenvironment .

For example , i f the number of data entries for the microenvironment is n and the number of data entries for the superordinate microenvironment is m, then the weight factor for the microenvironment may be n/ (n+m) , and the weight factor for the superordinate microenvironment may be m/ (n+m) . Alternatively, the weight factor for the microenvironment may be an/ (n+m) , and the weight factor for the superordinate microenvironment may be pm/ (n+m) , wherein a and p may optionally ful fill a boundary condition such as an+ pm = (n+m) . This way, microenvironments and superordinate microenvironment may be weighed di f ferently, depending on how much information is available . However, the key may be di f ferent , e . g . , more complex in that it may also take more microenvironment into account ( e . g . , several superordinate or in some other way neighboring microenvironment , and/or, in particular, a lowest order hierarchical microenvironment for which a suf ficient amount of data is available ) . For example in a chain of hierarchically structured microenvironments , weights may be assigned such as al , a2 , ..., an . The method may include assigning weights also to microenvironments that share superordinate microenvironments but are not directly related to each other ( in terms of allowing mutual access ) , and the like .

The method may comprise providing separate microenvironment IDs to the microenvironment and the superordinate microenvironment , and optionally, to further microenvironments . This may be an ef ficient way to promote the building step of the method in accordance with the present disclosure . This may promote building the structure for collecting data, in order to generate and output microenvironment-speci fic data, that is guided by the structure of a website or an app, and the like .

The providing ( e . g . , defining at the outset of applying the method to a particular website or app ) of a hierarchical structure of microenvironments may comprise providing at least one chain of microenvironments that are hierarchically structured in terms of rank in accordance with website or app build allowing a user to access a lower ranked microenvironment through a with respect thereto higher ranked microenvironment , the at least one chain running from a highest ranked microenvironment to a lowest ranked microenvironment .

A chain may constitute a series of ordered microenvironments in the sense of a highest ranked and a lowest ranked microenvironment being interceded by a series of microenvironments that are each between a superordinate and a subordinate microenvironment . A highest ranked microenvironment may, e . g . , correspond to a homepage of a website or a main page of an app . Website subsites or other app pages that can be successively reached can be associated with successively increasingly subordinate microenvironments .

Chains may cross , chains may have circular structures , and chains may split of f from other chains . This depends on the structure of a website or an app .

The method may comprise collecting first party data as microenvironment user data for one or several , or, optionally all of the microenvironments of one or several chains , based on first party data, such as uni fied IDs or net IDs , o f identi fied users accessing the respective microenvironments of the respective chain . In so far, the method may work analogously for any microenvironment as described above for an exemplary microenvironment . The identi fied users may in particular refer to users that have given their consent to the collection of user data . For example , the identi fied users may be identi fied by uni fied IDs or net IDs .

The method may comprise allocating di f ferent user-speci fic data for individual microenvironments of the chain, to di f ferent user groups , identi fying at least one predominant user group, optionally several predominant user groups , or a distribution of user groups , for the particular microenvironment and the microenvironment superordinate thereto , respectively, and, upon a request to access a particular microenvironment of the chain by an unidenti fied user, generating selective data output including at least one of user-speci fic data, allocated to the at least one predominant user group, optional ly to at least one of the several predominant user groups or at least one of the distribution of user groups , for the particular microenvironment , and user-speci fic data, allocated to the at least one predominant user group, optionally to at least one of the several predominant user groups or at least one of the distribution of user groups , for a microenvironment that is superordinate to the particular microenvironment . The latter microenvironment may be directly superordinate to the microenvironment or it may be superordinate to one or several additional microenvironments .

The identi fying may comprise applying a statistical method to the microenvironment user data of a particular microenvironment of the chain and a microenvironment superordinate thereto , to identi fy at least one predominant user group, optionally several predominant user groups , or a distribution of user groups , for the particular microenvironment and the microenvironment superordinate thereto , respectively .

The method may comprise veri fying for one or several , optionally for all of , the microenvironments of the chain whether there is suf ficient microenvironment user data for the respective microenvironment available , optionally by veri fying whether a number of data entries of first party data for the respective microenvironment is equal to or larger than a threshold .

The method may comprise , when there is a suf ficient amount of microenvironment user data available for a microenvironment , applying a statistical method to the microenvironment user data for the respective microenvironment to identi fy the at least one predominant user group, optionally the several predominant user groups or the distribution of user groups .

The identi fying need not depend on the suf ficient amount of microenvironment user data being available , but it is at least also performed when there it is available . In other words , the condition that it is performed when there is a suf ficient amount of data available , does not exclude for all embodiments that the identi fying may also already be performed before a suf ficient amount of data is available . However, for other embodiments , the identi fying is performed upon veri fying that a suf ficient amount of data is available . For those embodiments , the "when" may also be re-expressed as an " i f" or "upon the condition of" .

The method my comprise , upon a request to access the respective microenvironment of the chain by an unidenti fied user, when there is a suf ficient amount of microenvironment user data available for the respective microenvironment , generating selective data output including user-speci fic data, allocated to the at least one predominant user group, optionally to at least one of the several predominant user groups or at least one of the distribution of user groups , for the respective microenvironment , and when there is not a suf ficient amount of microenvironment user data available for the respective microenvironment , generating selective data output including user-speci fic data, allocated to the at least one predominant user group, optionally to at least one of the several predominant user groups or at least one of the distribution of user groups , for a superordinate microenvironment that is hierarchically superordinate to the respective microenvironment .

The method may comprise , upon the request to access the respective microenvironment of the chain by an unidenti fied user, iteratively identi fying, amongst microenvironments of the chain that are superordinate to the respective microenvironment , the lowest ranked superordinate microenvironment for which suf ficient microenvironment user data is available . This may be considered a resolution maximi zation in function of the current data respectively available . The lowest ranked microenvironment for which suf ficient data is available , is taken into account , and this may be the microenvironment that is being accessed itsel f . However, whenever there is insufficient data available , the next best solution is relied upon . This may be considered an ongoing dynamic microenvironment resolution maximi zation . This may particularly prevent digital waste in the sense o f decreasing the average data trans fer and, hence , reducing the overall data traf fic . This in turn may, on average , contribute to saving energy and resources and may thus be considered green technology .

The microenvironment , the superordinate microenvironment , and, optionally, each one of the at least one , several or all , of the microenvironments of the chain, may each be any one of a host , a domain name , a domain, sub-level domain, a path, and/or another location of a website , respectively .

The microenvironment , the superordinate microenvironment , and, optionally, each one of the at least one , several or all , of the microenvironments of the chain, may each be any one of a screen of an app, such as a log-in screen, a home screen, an onboarding screen, a profile screen, a settings screen, a feed screen, a calendar screen, a map screen, a conversational screen, a splash screen, a product card screen, a content screen, a checkout screen, a statistics screen, and a terms and conditions screen .

A microenvironment may belong to a top level domain, and the method may comprise providing a superordinate page ID to a microenvironment of another website or app as a hierarchically superordinate microenvironment to the microenvironment . This way, the method may be extended beyond a website or an app, in that another website or app may be used as superordinate with respect thereto . In so far, the method may be extended to larger structures and may thus even more ef fectively aid in reducing average data traf fic and, hence , digital waste .

The microenvironment may be a URL belonging to a top level domain, and the method may comprise providing predetermined microenvironmental data for the superordinate microenvironment , the superordinate microenvironment optionally being a URL or a group of URLs .

The generated microenvironment-speci fic output may comprise a part that is generated on the basis of the user speci fic data and a di f ferent part that is independent from the user speci fic data . This may increase the flexibi lity in terms of application and allow for adapting to a context .

Any one or several of the statistical methods mentioned above may comprise identi fying to which age group users belong and/or what the age of users is , and/or grouping users according to other categories included in IDs of the users . Any one or several statistical methods may comprise taking a middle value , a median value , and/or averaging an input in the form of data entries or parts thereof .

This disclosure also relates to a web server configured to execute the method in accordance with any one of or any combination of the preceding conf igurations . Some aspects o f the server may be understood to be functionally defined in terms of functional blocks configured to carry out respective method steps defined above .

For example , the web server may be configured to provide microenvironment-speci fic data output to an unidenti fied user for a website microenvironment or an app microenvironment in response to a user request of an unidenti fied user, in accordance with the present disclosure .

The web server may comprise a microenvironment user data building unit that buildings microenvironment user data for at least one microenvironment . The building of microenvironment user data is performed for the at least one microenvironment , and may be ( independently) performed for other microenvironments . The building of microenvironment user data may also be referred to as dynamically building microenvironment data, as the process is dynamic in that the building process may be continued whenever identi fied users access the microenvironment .

The microenvironment user data building unit may define ( this may be a providing) a hierarchical structure of microenvironments for a website or an app, the hierarchical structure including the at least one microenvironment . The hierarchical structure serves as a definition of a microenvironment being superordinate to another microenvironment . This may, in particular, correspond to the structure of a website or an app . For example , a homepage may correspond to a URL that is superordinate to a page ( a URL or a group of URLs ) that a user can access by clicking somewhere on the homepage . In the case of an app, a home screen may, e . g . , be superordinate to another screen .

The hierarchical structure may in particular define that there is a microenvironment is superordinate to the at least one microenvironment .

The web server may comprise a first party data collection unit that collects first party data as microenvironment user data for the at least one microenvironment , based on first party data of identi fied users accessing the at least one microenvironment . The identi fied users may in particular refer to users that have given their consent to the collection of user data . For example , the identified users may be identi fied by uni fied IDs or net IDs .

The web server may comprise a superordinate microenvironmentspeci fic data allocating unit that allocates superordinate microenvironment-speci fic data to the superordinate microenvironment . The allocating may or not include collecting first party data for the superordinate data, or a collecting of first party data may comprise the step of allocating data. The allocating data may in some cases be performed relying on data that is provided (without any collecting taking place as part of the method) . For example, data may be used that was collected in a different context (e.g., on the basis of a different app or website) .

The web server may comprise a user group identifying unit that defines user groups of identified users and allocates different user-specific data for the microenvironment to different user groups. The user groups may comprise different numbers of users, and a group may, e.g., also comprise only one user (or may, e.g., at times, comprise no users) .

The web server may comprise an identification unit that identifies at least one predominant user group, optionally several predominant user groups, or a distribution of user groups, for the microenvironment based on the microenvironment user data. The identifying may comprise applying a statistical method to the microenvironment user data.

The web server may comprise a microenvironment-specific data generation unit that generates, upon a request to access the microenvironment by an unidentified user, microenvironmentspecific data output including user-specific data, allocated to the at least one predominant user group, optionally to at least one of the several predominant user groups or at least one of the distribution of user groups, for the microenvironment .

The web server may comprise a microenvironment-specific data output unit that outputs, upon a request to access the microenvironment by an unidentified user, microenvironmentspecific data output including user-specific data, allocated to the at least one predominant user group, optionally to at least one of the several predominant user groups or at least one of the distribution of user groups , for the microenvironment .

The web server may comprise further units corresponding to one or several method steps discussed above . Reference is made to the respective description of the di f ferent aspects of the method, rather than repeating analogous explanations .

Some aspects of the web server may be understood to be defined as structural components placed in a physical location or in a plurality of physical locations . For example , a part of the server may be a cloud .

This disclosure also relates to a data carrier with a program stored thereon that , when run on a data processing unit , causes the data processing unit to execute the method of any one of or any combination of any configuration or aspect discussed above .

Additional advantages and features of the present disclosure , that can be reali zed on their own or in combination with one or several features discussed above , insofar as the features do not contradict each other, will become apparent from the following description of particular embodiments .

Brief Description of the Drawings

For a better understanding of the present disclosure and to show how the same may be carried into ef fect , reference will now be made , by way of example only, to the accompanying drawings , in which : Fig . 1A depicts a structure of a website to which an exemplary embodiment of the method in accordance with the present disclosure is to be applied;

Fig . IB depicts a hierarchical structure of two microenvironments of a website that are defined carrying out a method in accordance with the present disclosure ;

Fig . 2 is a flow diagram illustrating an embodiment of a method in accordance with the present disclosure ;

Fig . 3A depicts the structure of an app to which the method in accordance with the present disclosure is to be applied;

Fig . 3B depicts a hierarchical structure 5 of microenvironments of the app of Fig . 3A that are defined carrying out a method in accordance with the present disclosure ;

Fig . 4A depicts a hierarchical structure 5 of microenvironments of a website at a first point in time ;

Fig . 4B depicts a hierarchical structure 5 of microenvironments of the website of Fig . 4A at a second point in time ;

Fig . 5 depicts a hierarchical structure 5 of microenvironments of a website ; and

Fig . 6 depicts a hierarchical structure 5 of microenvironments of a website . DETAILED DESCRIPTION

Fig . 1A depicts a structure of a website to which an exemplary embodiment of the method in accordance with the present disclosure is to be applied . It comprises a home screen 1 and a sub screen 2 that can be accessed by a user via the home screen 1 .

Fig . IB depicts a hierarchical structure 5 of two microenvironments of the website of Fig . 1A that are defined carrying out a method in accordance with the present disclosure . A superordinate microenvironment 10 is assigned to the home screen 1 (which is in this case defined by a URL ) , and a subordinate microenvironment 20 is assigned to the sub screen 2 . The hierarchical structure is defined by one microenvironment being superordinate to the other one . The two microenvironments 10 and 20 may also be considered as a chain of two microenvironments , running from the superordinate microenvironment 10 to the subordinate microenvironment 20 .

Fig . 2 is a flow diagram illustrating an embodiment of a method in accordance with the present disclosure . This method will be applied with respect to the website , the structure of which is illustrated in Fig . 1A.

For completeness , it is to be mentioned that the method may include the step of , i f the website is accessed for the first time , build a hierarchical structure of microenvironments , as illustrated in Fig . IB . Alternatively, the hierarchical structure could already have been provided, or the method could involve assigning a part of the structure ( e . g . , only in as far as microenvironments have already been accessed) , and the like .

When a user accesses the home screen 1 , for example , the illustrated method will in a first step S I veri fy whether the users is an identi fied user or a non-identi f led user . If the user is a registered user, the method will proceed to step S2 and engage in the dynamical building of microenvironment user data for the microenvironment 10. In particular, first party data for this user is collected and stored as microenvironment user data. This user data is the microenvironment user data for the microenvironment 10. The data is in particular added to the already collected microenvironment user data. This may be considered an example of allocating user data to the home screen 1, as a superordinate microenvironment.

When a user accesses the sub screen 2, the illustrated method will likewise in a first step SI verify whether the users is an identified user or a non-identif led user.

If the user is a registered user, the method will proceed to step S2 and engage in the dynamical building of microenvironment user data for the microenvironment 20. In particular, first party data for this user is collected and stored as microenvironment user data. This user data is the microenvironment user data for the microenvironment 20. The data is in particular added to the already collected microenvironment user data.

In some embodiments, the method may alternatively, for example, only collect user data for the sub screen 2, but not for the home screen 1. User data for the home screen 1 may alternatively, e.g., be provided in a different manner.

In the case of the method illustrated in Fig. 2, user groups are formed according to the ages of the users and the gender. For example, the users may be divided into gender and into the groups associated with the ages of 14-18, 19-25, 26-30, 31-35, 36-40, 41-45, 46-50, 51-55, 56-60, 61-65, and 65+. However, this is just a single example of an infinite amount of possibilities . The method may also (e.g., initially, upon starting to apply the method to the particular website of Fig. 1A, or, e.g., periodically, or always when carrying out the discussed steps) , include allocating different user-specific data for the microenvironment to different user groups. For example, the male age group of 19-25 year olds may statistically be far more likely to see information relevant to the labor market for their age group, and/or news that is relevant to their age group, and the like. In turn, there may be explanations that are associated with the age group of 65+, so that there is additional navigation information for the website, and/or the structure of the choice menu may be simplified. This way, the overall average network traffic is reduced, and this allows to reduce digital waste and save energy and resources.

The method further involves identifying at least one predominant user group, optionally several predominant user groups, or a distribution of user groups, for the microenvironment based on the microenvironment user data for the microenvironment 10 and for the microenvironment 20, respectively. For example, the distribution may be identified to involve 40% users in the group of 19-25 male, 30% 19-25 female, 20% 26-30 female, and 10% rest for the microenvironment 10 and identify 70% 19-25 female, 15% 26-30 female, and 15% rest .

If, when a user accesses a microenvironment, for example, the microenvironment 20, the user is non-identif led (answer "N" when performing step SI) , the method proceeds to step S3. It then verifies whether a sufficient amount of microenvironment user data for the microenvironment 20 is available. This may, for example, be checked whether there is a sufficient number of entries (purely by way of example, the threshold may, e.g., be 80 entries) .

If this is answered affirmatively ("Y") , the method proceeds to step S4 and outputs microenvironment-specific data output including user-specific data, allocated to the at least one predominant user group, optionally to at least one of the several predominant user groups or at least one of the distribution of user groups, for the microenvironment 20. In other words, it will output the data based on what is allocated to 70% 19-25 female, 15% 26-30 female, and 15% rest. In a different embodiment, it may, e.g., exclusively rely on a predominant user group (here: 19-25 female) .

If this is answered negatively ("N") , i.e., if there is not a sufficient amount of user data available for the microenvironment 20, the method proceeds to step S5 and outputs microenvironment-specific data output including user-specific data, allocated to the at least one predominant user group, optionally to at least one of the several predominant user groups or at least one of the distribution of user groups, for the superordinate microenvironment 10. In other words, it will output the data based on what is allocated to 40% 19-25 male, 30% 19-25 female, 20% 26-30 female, and 10% rest. In a different embodiment, it may, e.g., exclusively rely on a predominant user group (here: 19-25 male) .

Fig. 3A depicts the structure of an app to which the method in in accordance with the present disclosure is to be applied. The app comprises a home screen 1 and a two directly accessible sub screens 2 and 3, as well as a screen 4 that can be accessed through the sub screen 2, as well as two further screens 6 and 7 that can be alternatively accessed from the screen 4.

Fig. 3B depicts a hierarchical structure 5 of microenvironments of the app of Fig. 3A that are defined carrying out a method in accordance with the present disclosure. A superordinate microenvironment 10 is assigned to the home screen 1 (which is in this case defined by a URL, as it is a web app) , and subordinate microenvironments 20 and 30 are assigned to the sub screens 2 and 3. Moreover, microenvironments 40, 60, and 70 are also assigned to the screens 4, 6, and 7.

The sequence of microenvironments 10, 20, 40, and 60 is a chain of microenvironments, running from the most superordinate microenvironment 10 to the most subordinate microenvironment 60. The sequence of microenvironments 10, 20, 40, and 70 is a different chain, running from the most superordinate microenvironment 10 to the most subordinate microenvironment 70. Also the sequence of microenvironments 10 and 30 is a chain .

When the user accesses a microenvironment, such as, for example, the microenvironment 60, the method carries out the following steps. First, the method (e.g., the one illustrated in Fig. 2) will in a first step SI verify whether the user is an identified user or a non-identif led user.

If the user is a registered user, the method will proceed to step S2 and engage in the dynamical building of microenvironment user data for the microenvironment 60. In particular, first party data for this user is collected and stored as microenvironment user data. This user data is the microenvironment user data for the microenvironment 60. The data is in particular added to the already collected microenvironment user data. This may be considered an example of allocating user data to the microenvironment 60.

The method further involves identifying at least one predominant user group for the microenvironment 60. It may, for example, be a male in the age group 65+ .

If a non-identif led user accesses the microenvironment 60, the method may proceed to step S3 and investigate whether there is a sufficient amount of microenvironment user data available for the microenvironment 60. This may, for example, be checked whether there is a sufficient number of entries (purely by way of example, the threshold may, e.g., be 80 entries) .

If this is answered affirmatively ("Y") , the method proceeds to step S4 and outputs microenvironment-specific data output including user-specific data, allocated to the at least one predominant user group (male 65+) , optionally to at least one of the several predominant user groups or at least one of the distribution of user groups, for the microenvironment 60.

If this is answered negatively ("N") , i.e., the method will proceed to an iterative approach starting from step S3 (the iteration is schematically illustrated by the dotted arrow line in Fig. 2) , by one hierarchy level in terms of the chain of microenvironments. It will, hence, first turn to the microenvironment 40. It will investigate whether there is a sufficient amount of microenvironment user data available for the microenvironment 40. If yes, the method proceeds to step S4 and outputs microenvironment-specific data output including user-specific data, allocated to the at least one predominant user group for the microenvironment 40.

If there is not a sufficient amount of user data available for the microenvironment 40, the method will return to step S3, as a next step in an iterative process of identifying the lowest ranked microenvironment with a sufficient amount of data available. It will thus now turn to the microenvironment 20.

The method thus will then investigate whether there is a sufficient amount of microenvironment user data available for the microenvironment 30. This may, for example, be checked whether there is a sufficient number of entries (purely by way of example, the threshold may, e.g., be 80 entries) .

If this is answered affirmatively ("Y") , the method proceeds to step S4 and outputs microenvironment-specific data output including user-specific data, allocated to the at least one predominant user group for the microenvironment 30.

If this is answered negatively ("N") , i.e., the method will turn to the microenvironment 10 (the home screen-associated microenvironment) . Depending on the embodiment, the method may, e.g., output microenvironment user data for the microenvironment 10 that was allocated thereto (e.g., collected or in some other manner assigned) . Alternatively, the hierarchical structure may involve that some other microenvironment is considered superordinate to the microenvironment 10 (e.g., a screen of a different app) . In that case, microenvironment user data would be considered for that superordinate microenvironment.

Fig. 4A depicts a hierarchical structure 5 of microenvironments of a website as provided in the framework of an embodiment of a method in accordance with the present disclosure, at a first point in time. Fig. 4B depicts a hierarchical structure 5 of microenvironments of the same website as provided in the framework of an embodiment of a method in accordance with the present disclosure, at a second point in time (later than the first point in time, when more data has been collected) .

As shown in Fig. 4A, there is a highest ranked microenvironment 10 that has been associated with a top URL of the website. There are four sub screens corresponding to four microenvironments 21, 22, 23, and 24 that can be alternatively accessed via the home screen (that is associated with the microenvironment 10) , and each of them allows accessing two respective sub screens associated with the microenvironments 31, 32 (through 21) , 33, 34 (through 22) , 35, 37 (through 23) , and 37, 38 (through 24) .

The circles in Fig. 4A illustrate the microenvironments 10, 21, and 22 for which a sufficient amount of microenvironment user data has been collected at the first point in time. At the first point in time, the method will, upon a nonregistered user accessing the microenvironments 10, 21, and 22 output microenvironment-specific data output including userspecific data, allocated to the at least one predominant user group, optionally to at least one of the several predominant user groups or at least one of the distribution of user groups, for the microenvironment 10, 21, or 22, respectively.

In contrast, if one of the microenvironments 23, 24, 35, 36, 37, and 38 is accessed by a non-registered user, output microenvironment-specific data output including user-specific data, allocated to the at least one predominant user group, optionally to at least one of the several predominant user groups or at least one of the distribution of user groups, for the microenvironment 10. The method will in the cases of microenvironments 35 and 36 arrive at the microenvironment 10 by the iterative approach sketched above.

If one the microenvironments 31, 32 is accessed, the method turns to the next/adj acent/proximate superordinate microenvironment in a chain of the hierarchical structure linking microenvironments 31, 32 to a superordinate tier, in the illustrated example, microenvironment 21, and if one the microenvironments 33, 34 is accessed, the method turns to the microenvironment 22.

Turning to Fig. 4B and, hence, to the situation at later point in time (a second point in time) , a sufficient amount of microenvironment user data has now also become available for the microenvironments 23, 24, 32, 34, and 35.

Thus, at the second point in time, upon a non-registered user accessing the microenvironments 10, 21, 22, 23, 24, 32, 34, or 35, the method will output microenvironment-specific data output including user-specific data, allocated to the at least one predominant user group, optionally to at least one of the several predominant user groups or at least one of the distribution of user groups, for the microenvironment 10, 21, 22, 23, 24, 32, 34, or 35, respectively.

In contrast, if the microenvironment 31 is accessed, the method turns to the microenvironment 21, if the microenvironment 33 is accessed, the method turns to the microenvironment 22, if the microenvironment 36 is accessed, the method turns to the microenvironment 23, and if one of the microenvironments 37 or 38 is accessed, the method turns to the microenvironment 24. All of the mentioned accesses here refer to the situation in which a non-identif led user accesses the respective microenvironment (if an identified user accesses the microenvironment, the data building step is carried out.

Fig. 5 depicts a hierarchical structure 5 of microenvironments of another website as provided in the framework of an embodiment of a method in accordance with the present disclosure. In the case of this structure 5, there are six different chains of microenvironments: 10-21; 10-22; 10-23-30- 40; 10-24; 10-25; and 10-26. A sufficient amount of microenvironment user data has been collected, in this example, for the microenvironments 10, 21, 22, 23, and 30. For these microenvironments, data output includes respective microenvironment-specific data upon access by a non-identif led user .

In contrast, upon an access of one of the remaining microenvironments by a non-identif led user, the lowest ranked micro-environment in a chain is identified, and the output is based thereon. For example, if the microenvironment 40 is accessed by a non-identif led user, user-specific data for the microenvironment 30 is output.

Fig. 6 depicts a hierarchical structure 5 of another website. In this case, there are the chains 10-21-31-40 and 10-22-32- 40 of microenvironments, and there is a sufficient amount of microenvironment user data available for the microenvironments 10, 21, 22, 31, and 33, but not yet for the microenvironment

Upon access of the microenvironment 40 of a non-identif led user, user-specific data is included in a data output that includes a part that is based 50% on user-specific data for the microenvironment 31 and 50% on user-specific data for the microenvironment 32. However, the key may be different for other embodiments. For example, depending on how much information is already available for the microenvironment 40, a part could be based thereon. For example, a part of the data output could comprise 25% user-specific data for the microenvironment when half of the sufficiency threshold is met in terms of entries, e.g., 40 out of 80 entries) and 10% can be based on microenvironment, 5% on microenvironment 21, and 15% on microenvironment 32. This is one of a large number of possibilities of distribution keys of weights of the influence from different neighboring microenvironments.

FIG. 7 illustrates an example system 100. In particular embodiments, one or more computing devices of system 100 perform one or more steps of one or more methods described or illustrated herein. In particular embodiments, one or more systems 100 provide functionality described or illustrated herein. In particular embodiments, software running on one or more computing devices of system 100 performs one or more steps of one or more methods described or illustrated herein or provides functionality described or illustrated herein. Particular embodiments include one or more portions of one or more systems 100. Herein, reference to a computer system may encompass a computing device, and vice versa, where appropriate. Moreover, reference to a computer system may encompass one or more computer systems, where appropriate.

In the illustrated example, system 100 includes a server 102 in communication over a network 104 with at least one computing device 106 and/or at least one mobile device 108. Although FIG . 7 shows only one computing device 106 and only one mobile device 108 , system 100 may include any number of either in communication with one or more of servers 102 for interacting with website microenvironments .

Server 102 may be configured to host or generate website microenvironments for display on computing device 106 and/or mobile device 108 and be configured to execute any of the corresponding methods disclosed herein . In the illustrated example , server 102 includes one or more applications , including a microenvironment user data builder application 110 and a microenvironment-speci fic data output application 112 .

Microenvironment user data builder application 110 may include instructions for causing one or more processors 114 to perform operations including dynamically building microenvironment user data for at least one microenvironment according to any of the methods disclosed herein . In some examples , microenvironment user data builder application 110 may include instructions for providing a hierarchical structure 116 of microenvironments 118 , which may be stored in a memory 120 of server 102 . The microenvironments may be for a website for display on a computing device 106 or a mobile app for display on a mobile device 108 . In some examples , the hierarchical structure 116 may include at least one microenvironment 118 stored in memory 120 , such as a URL, a group of URLS , or a path, and a superordinate microenvironment 118 , such as a URL, a group of URLS , a path, or a domain name , that is defined to be hierarchically superordinate to the microenvironment . In some examples , the user data builder application 110 may also include instructions for collecting first party data as microenvironment user data 122 for the at least one microenvironment 116 , based on first party data of identi fied users accessing the at least one microenvironment . In some examples , the user data builder application 110 may also include instructions for allocating superordinate microenvironment-speci fic data to the superordinate microenvironment , providing user groups of identi fied users and allocating di f ferent user-speci fic data for the microenvironment to di f ferent user groups , and identi fying at least one predominant user group, optionally several predominant user groups , or a distribution of user groups , for the microenvironment based on the microenvironment user data .

Microenvironment-speci fic data output application 112 may include instructions for causing one or more processors 114 to perform operations including, upon a request to access the microenvironment by an unidenti fied user, generating and outputting microenvironment-speci fic data output 124 , which may be stored in and accessed from memory 120 , including userspeci fic data, allocated to the at least one predominant user group, optionally to at least one of the several predominant user groups or at least one of the distribution of user groups , for the microenvironment .

In particular embodiments , the computing devices of system 100 each include one or more processors 114 , memory 120 , and a communication interface 130 . Computing device 106 and mobile device 108 also include applications 140 which include applications for interacting with microenvironments disclosed herein, including graphically displaying microenvironments on displays 150 , such as web browsers and mobile applications . Although this disclosure describes and illustrates a particular computer system having a particular number o f particular components in a particular arrangement , this disclosure contemplates any suitable computer system having any suitable number of any suitable components in any suitable arrangement .

In particular embodiments , processor 114 includes hardware for executing instructions , such as those making up a computer program . As an example and not by way of limitation, to execute instructions , processor 114 may retrieve ( or fetch) the instructions from an internal register, an internal cache , or memory 120 ; decode and execute them; and then write one or more results to an internal register, an internal cache , or memory 120 . In particular embodiments , processor 114 may include one or more internal caches for data, instructions , or addresses . This disclosure contemplates processor 114 including any suitable number of any suitable internal caches , where appropriate . Although this disclosure describes and illustrates a particular processor, this disclosure contemplates any suitable processor .

In particular embodiments , memory 120 includes main memory for storing instructions for processor 114 to execute or data for processor 114 to operate on . In particular embodiments , one or more memory management units (MMUs ) reside between processor 114 and memory 120 and facilitate accesses to memory 120 requested by processor 114 . In particular embodiments , memory 120 includes random access memory (RAM) including dynamic RAM ( DRAM) or static RAM ( SRAM) . Memory 120 may include one or more memories 120 , where appropriate . Although this disclosure describes and illustrates particular memory, this disclosure contemplates any suitable memory . Computing devices of system 100 may also include mass storage for data or instructions within memory 120 or in addition to the memory in additional storage devices . As an example and not by way of limitation, storage may include a hard disk drive (HDD) , a floppy disk drive , flash memory, an optical disc, a magneto-optical disc, magnetic tape , or a Universal Serial Bus (USB ) drive or a combination of two or more of these . Storage may include removable or non-removable ( or fixed) media, where appropriate . Storage may be internal or external to one or more computing devices of system 100 , where appropriate . In particular embodiments , storage is non-volatile , solid-state memory . In particular embodiments , storage includes read-only memory (ROM) . Where appropriate , this ROM may be maskprogrammed ROM, programmable ROM ( PROM) , erasable PROM (EPROM) , electrically erasable PROM (EEPROM) , electrically alterable ROM (EAROM) , or flash memory or a combination of two or more of these .

In particular embodiments , communication interface 1110 includes hardware , software , or both providing one or more interfaces for communication ( such as , for example , packetbased communication) between the computing devices of system 100 over one or more networks 104 . As an example and not by way of limitation, communication interface 1110 may include a network interface controller (NIC ) or network adapter for communicating with an Ethernet or other wire-based network or a wireless NIC (WNIC ) or wireless adapter for communicating with a wireless network, such as a WI-FI network . Thi s disclosure contemplates any suitable network and any suitable communication interface 1110 for it . As an example and not by way of limitation, computing devices of system 100 may communicate with an ad hoc network, a personal area network ( PAN) , a local area network ( LAN) , a wide area network (WAN) , a metropolitan area network (MAN) , or one or more portions of the Internet or a combination of two or more of these . One or more portions of one or more of these networks may be wired or wireless . As an example , computing devices of system 100 may communicate with a wireless PAN (WPAN) ( such as , for example , a BLUETOOTH WPAN) , a WI-FI network, a WI-MAX network, a cellular telephone network ( such as , for example , a Global System for Mobile Communications ( GSM) network) , or other suitable wireless network or a combination of two or more of these . Computing devices of system 100 may include any suitable communication interface 1110 for any of these networks , where appropriate . Communication interface 1110 may include one or more communication interfaces 1110 , where appropriate . Although this disclosure describes and illustrates a particular communication interface , this disclosure contemplates any suitable communication interface .

Herein, a computer-readable non-transitory storage medium or media may include one or more semiconductor-based or other integrated circuits (ICs) (such, as for example, field- programmable gate arrays (FPGAs) or application-specific ICs (ASICs) ) , hard disk drives (HDDs) , hybrid hard drives (HHDs) , optical discs, optical disc drives (ODDs) , magneto-optical discs, magneto-optical drives, floppy diskettes, floppy disk drives (FDDs) , magnetic tapes, solid-state drives (SSDs) , RAM- drives, SECURE DIGITAL cards or drives, any other suitable computer-readable non-transitory storage media, or any suitable combination of two or more of these, where appropriate. A computer-readable non-transitory storage medium may be volatile, non-volatile, or a combination of volatile and non-volatile, where appropriate.

It will be apparent to those skilled in the art that various modifications and variations can be made in the disclosed devices and systems without departing from the scope of the disclosure. Other aspects of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the features disclosed herein. It is intended that the specification and examples be considered as exemplary only. Many additional variations and modifications are possible and are understood to fall within the framework of the disclosure.

Claims

1 . A method of providing microenvironment-speci fic data output to an unidenti fied user for a website microenvironment or an app microenvironment , such as a URL or a group of URLs of a website or an app screen, in response to a user request of an unidenti fied user, the method comprising : dynamically building microenvironment user data for at least one microenvironment , involving the steps of :

- providing a hierarchical structure of microenvironments for a website or an app, the hierarchical structure including at least one microenvironment , such as a URL, a group of URLS , or a path, and a superordinate microenvironment , such as a URL, a group of URLS , a path, or a domain name , that is defined to be hierarchically superordinate to the micro environment ,

- collecting first party data as microenvironment user data for the at least one microenvironment , based on first party data of identi fied users accessing the at least one microenvironment ;

- allocating superordinate microenvironment-speci fic data to the superordinate microenvironment ;

- providing user groups of identi fied users and allocating di f ferent user-specific data for the microenvironment to di f ferent user groups ;

- identi fying at least one predominant user group, optionally several predominant user groups , or a distribution of user groups , for the microenvironment based on the microenvironment user data ; and the method comprising : upon a request to access the microenvironment by an unidenti fied user, generating and outputting microenvironment-speci fic data output including userspeci fic data, allocated to the at least one predominant user group, optionally to at least one of the several predominant user groups or at least one of the distribution of user groups , for the microenvironment .

2 . The method of claim 1 , comprising upon the request to access the microenvironment by an unidenti fied user, generating and outputting microenvironment-speci fic data output including at least one o f : user-speci fic data, allocated to the at least one predominant user group, optionally to at least one of the several predominant user groups or at least one of the distribution of user groups , for the microenvironment , and superordinate microenvironment-speci fic data allocated to the superordinate microenvironment .

3 . The method of claim 1 or 2 , comprising applying a statistical method to the microenvironment user data to identi fy the at least one predominant user group , optionally several predominant user groups , or a distribution of user groups , for the microenvironment .

4 . The method of any one of the preceding claims , comprising the step of veri fying whether there is a suf ficient amount of microenvironment user data avai lable for the at least one microenvironment , optionally by veri fying whether a number of data entries of first party data for the microenvironment is equal to or larger than a threshold, and when there is a suf ficient amount of microenvironment user data available , applying a statistical method to the microenvironment user data for the microenvironment to identi fy the at least one predominant user group , optionally the several predominant user groups or the distribution of user groups .

5 . The method of claim 4 , comprising, upon the request to access the microenvironment by the unidenti fied user, when there is a suf ficient amount of microenvironment user data available , generating the microenvironmentspeci fic data output such that it includes user-speci fic data allocated to the at least one predominant user group, optionally at least one of the several predominant user groups at least one of the distribution of user groups , and when there is not a suf ficient amount of microenvironment user data available , generating the microenvironmentspeci fic data output such that it includes superordinate microenvironment-speci fic data .

6 . The method of any one of the preceding claims , comprising allocating di f ferent superordinate microenvironmentspeci fic data to di f ferent user groups , to define di f ferent user-speci fic data for the superordinate microenvironment .

7 . The method of claim 6 , comprising collecting first party data as superordinate microenvironment user data for the superordinate microenvironment based on first party data of identi fied users accessing the superordinate micro environment , applying the statistical method or another statistical method to the superordinate microenvironment user data to identi fy at least one predominant user group, optionally several predominant user groups , or a distribution o f user groups , for the superordinate microenvironment , and comprising, upon the request to access the microenvironment by the unidenti fied user, when there i s not a suf ficient amount of microenvironment user data available , generating the microenvironment-speci fic data output such that it includes user-speci fic data, allocated to the at least one predominant user group, optionally to at least one of the several predominant user groups or at least one of the distribution of user groups , for the superordinate microenvironment .

8 . The method of claim 7 , wherein a weight factor is assigned to the microenvironment user data of the microenvironment and a superordinate weight factor is assigned to the superordinate microenvironment user data of the superordinate microenvironment , and wherein the at least one predominant user group, optionally the several predominant user groups or the distribution of user groups , for the microenvironment is identi fied on the basis of the microenvironment user data weighted by the weight factor and the superordinate microenvironment user data weighted by the superordinate weight factor .

9 . The method of claim 8 , wherein the weight factor is set in function of a number of data entries of first party data for the microenvironment .

10 . The method of any one of the preceding claims , providing separate microenvironment IDs to the microenvironment and the superordinate microenvironment , and optionally, to further microenvironments .

11 . The method of any one of the preceding claims , wherein the providing a hierarchical structure of microenvironments comprises providing at least one chain of microenvironments that are hierarchically structured in terms of rank in accordance with website or app build allowing a user to access a lower ranked microenvironment through a with respect thereto higher ranked microenvironment , the at least one chain running from a highest ranked microenvironment to a lowest ranked micro environment , wherein the method comprises :

- collecting first party data as microenvironment user data for the microenvironments of the chain, based on first party data, such as uni fied IDs or net IDs , of identi fied users accessing the respective microenvironments of the chain,

- allocating di f ferent user-speci fic data for individual microenvironments of the chain, to di f ferent user groups ;

- applying a statistical method to the microenvironment user data of a particular microenvironment of the chain and a microenvironment superordinate thereto , to identi fy at least one predominant user group, optionally several predominant user groups , or a distribution of user groups , for the particular microenvironment and the microenvironment superordinate thereto , respectively;

- upon a request to access a particular microenvironment of the chain by an unidenti fied user, generating selective data output including at least one of : user-speci fic data, allocated to the at least one predominant user group, optionally to at least one of the several predominant user groups or at least one of the distribution of user groups , for the particular microenvironment , and user-speci fic data, allocated to the at least one predominant user group, optionally to at least one of the several predominant user groups or at least one of the distribution of user groups , for a microenvironment that is superordinate to the particular microenvironment .

12 . The method of claim 11 , comprising veri fying for one or several , optionally for all of , the microenvironments o f the chain whether there is suf ficient microenvironment user data for the respective microenvironment available , optionally by veri fying whether a number of data entries of first party data for the respective microenvironment is equal to or larger than a threshold, and when there is a suf ficient amount of microenvironment user data available for a microenvironment , applying a statistical method to the microenvironment user data for the respective microenvironment to identi fy the at least one predominant user group, optionally the several predominant user groups or the distribution of user groups .

13 . The method of claim 12 , comprising, upon a request to access the respective microenvironment of the chain by an unidenti fied user, when there is a suf ficient amount of microenvironment user data available for the respective microenvironment , generating selective data output including user-speci fic data, allocated to the at least one predominant user group, optionally to at least one of the several predominant user groups or at least one of the distribution of user groups , for the respective microenvironment , and when there is not a suf ficient amount of microenvironment user data available for the respective microenvironment , generating selective data output including user-speci fic data, allocated to the at least one predominant user group, optionally to at least one of the several predominant user groups or at least one of the distribution of user groups , for a superordinate microenvironment that is hierarchically superordinate to the respective microenvironment .

14 . The method of claim 13 , comprising, upon the request to access the respective microenvironment of the chain by an unidenti fied user, iteratively identi fying, amongst microenvironments of the chain that are superordinate to the respective microenvironment, the lowest ranked superordinate microenvironment for which suf ficient microenvironment user data is available .

15 . The method of any one of the preceding claims , wherein the microenvironment , the superordinate microenvironment , and, optionally, each one of the at least one , several or all , of the microenvironments of the chain, are each any one of a host , a domain name , a domain, sub-level domain, a path, and/or another location of a website , respectively, or a screen of an app, such as a log-in screen, a home screen, an onboarding screen, a profile screen, a settings screen, a feed screen, a calendar screen, a map screen, a conversational screen, a splash screen, a product card screen, a content screen, a checkout screen, a statistics screen, and a terms and conditions screen .

16 . The method of any one of the preceding claims , wherein the microenvironment belongs to a top level domain and the method comprises : providing a superordinate page ID to a microenvironment of another website or app as a hierarchically superordinate microenvironment to the microenvironment .

17 . The method of any one of the preceding claims , wherein the microenvironment is a URL belongs to a top level domain and the method comprises providing predetermined microenvironmental data for the superordinate microenvironment , the superordinate microenvironment optionally being a URL or a group of URLs .

18 . The method of any one of the preceding claims , wherein the generated microenvironment-speci fic output comprises a part that is generated on the basis of the user speci fic data and a di f ferent part that is independent from the user speci fic data .

19 . The method of any one of the preceding claims , wherein the statistical method comprises identi fying to which age group users belong and/or what the age of users is , and/or grouping users according to other categories included in IDs of the users .

20. A web server configured to execute the method in accordance with any one of the preceding claims.

21. A data carrier with a program stored thereon that, when run on a data processing unit, causes the data processing unit to execute the method of any one of claims 1 to 19.